Recent developments in computers and corresponding speech recognition software algorithms have made it possible to control computer equipment via spoken input. Thus, it is now becoming more common that users are able to control their computers, electronics, personal devices, call routing, etc., via speech input.
Speech recognition systems are highly complex and operate by matching an acoustic signature of an utterance with acoustic signatures of words in a language model. As an example, according to conventional speech recognition systems, a microphone first converts a received acoustic signature of an uttered word into an electrical signal. An A/D (analog-to-digital) converter is typically used to convert the electrical signal into a digital representation of the uttered word. A digital signal processor converts the captured electrical signal from the time domain to the frequency domain.
Generally, as another part of the speech recognition process, the digital signal processor breaks down the utterance into its spectral components. Typically, the amplitude or intensity of the digital signal at various frequencies and temporal locations are then compared to a language model to determine the one or more words that were uttered.
Certain conventional speech recognition systems can be used for classifying utterances. For example, a conventional speech recognition system can receive and convert an utterance into respective text. In certain instances, a conventional speech recognition system can be configured to classify the utterance based on a key word in the utterance.